Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms

Parkinson’s disease(PD)is a neurodegenerative disordercharacterized by motor retardation,myotonia,quiescent tremor,and postural gait abnormality,as well as nonmotor symptoms such asanxiety and depression.Biofeedback improves motor and nonmotorfunctions of patients by regulating abnormal electroencephalogram(EEG),electrocardiogram(ECG),photoplethysmography(PPG),electromyography(EMG),respiration(RSP),or other physiologicalsignals.Given that multimodal signals are closely related to PDstates,the clinical effect of multimodal biofeedback on patientswith PD is worth exploring.Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into threegroups:multimodal(EEG,ECG,PPG,and RSP feedback signal),EEG(EEG feedback signal),and sham(random feedback signal),and they received biofeedback training five times in two weeks.The combined clinical scale and multimodal signal analysis resultsrevealed that the EEG group significantly improved motor symptomsand increased Berg balance scale scores by regulatingβband activity;the multimodal group significantly improved nonmotorsymptoms and reduced Hamilton rating scale for depression scores by improvingθband activity.Our preliminary results revealed thatmultimodal biofeedback can improve the clinical symptoms of PD,but the regulation effect on motor symptoms is weaker than that ofEEG biofeedback.

Review of brain-computer interface based on steady-state visual evoked potential

The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSVEP)is the most researched BCI experimental paradigm,which offers the advantages of high signal-to-noise ratio and short training-time requirement by users.In a complete BCI system,the two most critical components are the experimental paradigm and decoding algorithm.However,a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies.In the present study,the transient visual evoked potential,SSVEP,and various improved SSVEP paradigms are compared and analyzed,and the problems and development bottlenecks in the experimental paradigm are finally pointed out.Subsequently,the canonical correlation analysis and various improved decoding algorithms are introduced,and the opportunities and challenges of the SSVEP decoding algorithm are discussed.